Aurélie C. Lozano

According to our database, Aurélie C. Lozano authored at least 61 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Larimar: Large Language Models with Episodic Memory Control.

Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen

CoRR, 2024

ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation.

Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

CoRR, 2024

Structure-Informed Protein Language Model.

Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

CoRR, 2024

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes.

Dongxia Wu, Tsuyoshi Idé, Aurélie C. Lozano, Georgios Kollias, Jirí Navrátil, Naoki Abe, Yi-An Ma, Rose Yu

CoRR, 2024

2023

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.

Aurélie C. Lozano, Hantian Ding, Naoki Abe, Alexander E. Lipka

BMC Bioinform., December, 2023

Enhancing Protein Language Models with Structure-based Encoder and Pre-training.

Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

CoRR, 2023

Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction.

Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang

CoRR, 2023

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction.

Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Protein Representation Learning by Geometric Structure Pretraining.

Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks.

Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurélie C. Lozano, Qi Li

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

AlphaFold Distillation for Improved Inverse Protein Folding.

Igor Melnyk, Aurélie C. Lozano, Payel Das, Vijil Chenthamarakshan

CoRR, 2022

AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning.

Jihun Yun, Aurélie C. Lozano, Eunho Yang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Directed Graph Auto-Encoders.

Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Benchmarking deep generative models for diverse antibody sequence design.

Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurélie C. Lozano

CoRR, 2021

Adaptive Proximal Gradient Methods for Structured Neural Networks.

Jihun Yun, Aurélie C. Lozano, Eunho Yang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

A General Family of Stochastic Proximal Gradient Methods for Deep Learning.

Jihun Yun, Aurélie C. Lozano, Eunho Yang

CoRR, 2020

2019

Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models.

Ming Yu, Karthikeyan Natesan Ramamurthy, Addie M. Thompson, Aurélie C. Lozano

Frontiers Big Data, 2019

A graph Laplacian prior for Bayesian variable selection and grouping.

Sounak Chakraborty, Aurélie C. Lozano

Comput. Stat. Data Anal., 2019

Stochastic Gradient Methods with Block Diagonal Matrix Adaptation.

Jihun Yun, Aurélie C. Lozano, Eunho Yang

CoRR, 2019

Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning.

Jihun Yun, Peng Zheng, Eunho Yang, Aurélie C. Lozano, Aleksandr Y. Aravkin

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm.

Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel

Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

2017

How to foster innovation: A data-driven approach to measuring economic competitiveness.

Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao, C. Reddy, Aleksandra Mojsilovic, Kush R. Varshney

IBM J. Res. Dev., 2017

Generalized Kalman smoothing: Modeling and algorithms.

Aleksandr Y. Aravkin, James V. Burke, Lennart Ljung, Aurélie C. Lozano, Gianluigi Pillonetto

Autom., 2017

Learning Task Clusters via Sparsity Grouped Multitask Learning.

Meghana Kshirsagar, Eunho Yang, Aurélie C. Lozano

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity.

Eunho Yang, Aurélie C. Lozano

Proceedings of the 34th International Conference on Machine Learning, 2017

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World.

Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations.

Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing

J. Comput. Biol., 2016

Understanding Innovation to Drive Sustainable Development.

Prasanna Sattigeri, Aurélie C. Lozano, Aleksandra Mojsilovic, Kush R. Varshney, Mahmoud Naghshineh

CoRR, 2016

Stable estimation of Granger-causal factors of country-level innovation.

Aurélie C. Lozano, Prasanna Sattigeri, Aleksandra Mojsilovic, Kush R. Varshney

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion.

Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurélie C. Lozano

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Closed-form Estimators for High-dimensional Generalized Linear Models.

Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso.

Eunho Yang, Aurélie C. Lozano

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations.

Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire

IEEE Trans. Inf. Theory, 2014

Sparse Quantile Huber Regression for Efficient and Robust Estimation.

Aleksandr Y. Aravkin, Anju Kambadur, Aurélie C. Lozano, Ronny Luss

CoRR, 2014

Elementary Estimators for Graphical Models.

Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.

Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar

Proceedings of the 31th International Conference on Machine Learning, 2014

Elementary Estimators for High-Dimensional Linear Regression.

Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar

Proceedings of the 31th International Conference on Machine Learning, 2014

Orthogonal Matching Pursuit for Sparse Quantile Regression.

Aleksandr Y. Aravkin, Aurélie C. Lozano, Ronny Luss, Prabhanjan Kambadur

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality.

Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance.

Aurélie C. Lozano, Huijing Jiang, Xinwei Deng

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions.

Prabhanjan Kambadur, Aurélie C. Lozano

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference

Vikas Sindhwani, Aurélie C. Lozano, Ha Quang Minh

CoRR, 2012

A Bayesian Markov-switching Model for Sparse Dynamic Network Estimation.

Huijing Jiang, Aurélie C. Lozano, Fei Liu

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Multi-level Lasso for Sparse Multi-task Regression.

Aurélie C. Lozano, Grzegorz Swirszcz

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Group Orthogonal Matching Pursuit for Logistic Regression.

Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery.

Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu

J. Bioinform. Comput. Biol., 2011

Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels.

Vikas Sindhwani, Aurélie C. Lozano

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference.

Aurélie C. Lozano, Vikas Sindhwani

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Learning Temporal Causal Graphs for Relational Time-Series Analysis.

Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Grouped graphical Granger modeling for gene expression regulatory networks discovery.

Aurélie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset

Bioinform., 2009

Proximity-Based Anomaly Detection Using Sparse Structure Learning.

Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe, Yan Liu

Proceedings of the SIAM International Conference on Data Mining, 2009

Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction.

Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Spatial-temporal causal modeling for climate change attribution.

Aurélie C. Lozano, Hongfei Li, Alexandru Niculescu-Mizil, Yan Liu, Claudia Perlich, Jonathan R. M. Hosking, Naoki Abe

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Grouped graphical Granger modeling methods for temporal causal modeling.

Aurélie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

A data modeling approach to climate change attribution.

Aurélie C. Lozano

Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, 2009

2008

Multi-class cost-sensitive boosting with p-norm loss functions.

Aurélie C. Lozano, Naoki Abe

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

2007

Throughput scaling in wireless networks with restricted mobility.

Aurélie C. Lozano, Sanjeev R. Kulkarni, Pramod Viswanath

IEEE Trans. Wirel. Commun., 2007

2006

Convergence and Consistency of Recursive Boosting.

Aurélie C. Lozano, Sanjeev R. Kulkarni

Proceedings of the Proceedings 2006 IEEE International Symposium on Information Theory, 2006

2005

Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations.

Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

A wireless network can achieve maximum throughput without each node meeting all others.

Aurélie C. Lozano, Sanjeev R. Kulkarni

Proceedings of the 2005 IEEE International Symposium on Information Theory, 2005

2002

Quantized Frame Expansions In A Wireless Environment.

Aurélie C. Lozano, Jelena Kovacevic, Mike Andrews

Proceedings of the 2002 Data Compression Conference (DCC 2002), 2002